Method and system for adaptively varying templates to accommodate changes in biometric information

ABSTRACT

A method of authenticating a user in dependence upon biometric input information is disclosed. According to the method when a user is identified, their biometric information or data derived therefrom is automatically stored. The data is then used in subsequent user identification attempts as a template. The original templates are not replaced but those templates that are automatically stored are replaced at intervals.

FIELD OF THE INVENTION

[0001] The invention relates generally to biometric security systems andmore particularly to a method of automatically updating biometrictemplates based on varying aspects of biometric information providedfrom a same biometric information source.

BACKGROUND OF THE INVENTION

[0002] Computer security is fast becoming an important issue. With theproliferation of computers and computer networks into all aspects ofbusiness and daily life—financial, medical, education, government, andcommunications—the concern over secure file access is growing. Usingpasswords is a common method of providing security. Password protectionand/or combination type locks are employed for computer networksecurity, automatic teller machines, telephone banking, calling cards,telephone answering services, houses, and safes. These systems generallyrequire the knowledge of an entry code that has been selected by a useror has been preset.

[0003] Preset codes are often forgotten, as users have no reliablemethod of remembering them. Writing down the codes and storing them inclose proximity to an access control device (i.e. The combination lock)results in a secure access control system with a very insecure code.Alternatively, the nuisance of trying several code variations rendersthe access control system more of a problem than a solution.

[0004] Password systems are known to suffer from other disadvantages.Usually, passwords are specified by a user. Most users, beingunsophisticated users of security systems, choose passwords that arcrelatively insecure. As such, many password systems are easily accessedthrough a simple trial and error process.

[0005] A security access system that provides substantially secureaccess and does not require a password or access code is a biometricidentification system. A biometric identification system accepts uniquebiometric information from a User and identifies the user by matchingthe information against information belonging to registered users of thesystem. One such biometric identification system is a fingerprintrecognition system.

[0006] In a fingerprint input transducer or sensor, the finger underinvestigation is usually pressed against a flat surface, such as a sideof a glass plate; the ridge and valley pattern of the finger tip issensed by a sensing means such as an interrogating light beam.

[0007] Various optical devices arc known which employ prisms upon whicha finger whose print is to be identified is placed. The prism has afirst surface upon which a finger is placed, a second surface disposedat an acute angle to the first surface through which the fingerprint isviewed and a third illumination surface through which light is directedinto the prism. In some cases, the illumination surface is at an acuteangle to the first surface, as seen for example, in U.S. Pat. Nos.5,187,482 and 5,187,748. in other cases, the illumination surface isparallel to the first surface, as seen for example, in U.S. Pat. Nos.5.109,427 and 5,233,404. Fingerprint identification devices of thisnature are generally used to control the building-access orinformation-access of individuals to buildings, rooms, and devices suchas computer terminals.

[0008] U.S. Pat. No. 4,353,056 in the name of Tsikos issued Oct. 5,1982, discloses an alternative kind of fingerprint sensor that uses acapacitive sensing approach. The described sensor has a two dimensional,row and column, array of capacitors, each comprising a pair of spacedelectrodes, carried in a sensing member and covered by an insulatingfilm. The sensors rely upon deformation to the sensing member caused bya finger being placed thereon so as to vary locally the spacing betweencapacitor electrodes, according to the ridge/trough pattern of thefingerprint, and hence, the capacitance of the capacitors. In onearrangement, the capacitors of each column are connected in series withthe columns of capacitors connected in parallel and a voltage is appliedacross the columns. In another arrangement, a voltage is applied to eachindividual capacitor in the array. Sensing in the respective twoarrangements is accomplished by detecting the change of voltagedistribution in the series connected capacitors or by measuring thevoltage values of the individual capacitances resulting from localdeformation. To achieve this, an individual connection is required fromthe detection circuit to each capacitor.

[0009] Before the advent of computers and imaging devices, research wasconducted into fingerprint characterisation and identification. Today,if much of the research focus in biometrics has been directed towardimproving the input transducer and the quality of the biometric inputdata. Fingerprint characterization is well known and can involve manyaspects of fingerprint analysis. The analysis of fingerprints isdiscussed in the following references, which are hereby incorporated byreference:

[0010] Xiao Qinghan and Bian Zhaoqi,: An approach to FingerprintIdentification By Using the Attributes of Feature Lines of Fingerprint,”IEEE Pattern Recognition, pp 663, 1986;

[0011] C. B. Shelman, “Fingerprint Classification—Theory andApplication,” Proc. 76 Carnahan Conference on Electronic CrimeCountermeasures, 1970:

[0012] Feri Pernus, Stanko Kovacic, and Ludvik Gyergyek, “Minutaic BasedFingerprint Registration,” IEEE Pattern Recognition. pp 1380, 1980;

[0013] J. A. Ratkovic, F. W. Blackwell, and H. H. Bailey, “Concepts fora Next Generation Automated Fingerprint System,” Proc. 78 CarnahanConference on Electronic Crime Countermeasures, 1978,

[0014] K. Millard, “An approach to the Automatic Retrieval of LatentFingerprints,” Proc. 75 Carnahan Conference on Electronic CrimeCountermeasures, 1975;

[0015] Moayer and K. S. Fu, “A Syntactic Approach to Fingerprint PatternRecognition,” Memo Np. 73-18, Purdue University, School of ElectricalEngineering, 1973;

[0016] Wegstein, An Automated Fingerprint Identification System, NBSspecial publication, U.S. Department of Commerce/National Bureau ofStandards, ISSN 0083-1883; no. 500-89, 1982;

[0017] Moenssens, Andre A., Fingerprint Techniques, Chilton Book; Co.,1971: and,

[0018] Wegstein and J. F. Rafferty, The LX39 Latent Fingerprint Matcher,NBS special publication, U.S. Department of Commerce/National Bureau ofStandards; no. 500-36, 1978.

[0019] In the past, user authorization based on biometric informationwas conducted by correlating a single instance of biometric informationagainst a template. By using th is method, a percentage of thepopulation is difficult to authenticate. Further, due to skin damage andinjuries, sometimes biometric information is not suited toidentification. A sore throat affecting voice information and scrapedfingertips affecting fingerprint information are two examples of commonproblems with authorization in dependence upon biometric information.

[0020] Biometric information is commonly subject to minor variationsover time. For example, as the temperature drops below freezing, the airbecomes much more dry. With the dry weather comes drier skin. Somepeople experience significant problems with fingerprint readers whentheir skin varies with changing weather conditions. It would beadvantageous to provide a biometric identification system thatautomatically compensates for variations that result over time.

OBJECT OF THE INVENTION

[0021] It is an object of this invention to provide a method forautomatically compensating for variations in biometric information thatresult over time or are temporary in nature.

SUMMARY OF THE INVENTION

[0022] In accordance with the invention there is provided a method ofidentifying a user comprising the steps of:

[0023] a) receiving, a biometric information sample from the user foruse in identifying the user;

[0024] b) determining electronic data representing said biometricinformation sample of the user;

[0025] c) analyzing the electronic data to compare the electronic dataagainst first stored data relating to identifications of users;

[0026] d) when a substantial match is found, identifying the individual;and,

[0027] e) when a difference between the electronic data and the firststored data is above a predetermined other threshold, comparing theelectronic data against adaptive enrollments, the adaptive enrollmentsrelating to biometric information samples provided by the user for usein identifying the user by the steps a) through (e).

[0028] In accordance with the invention there is also provided a methodof identifying a user comprising the steps of:

[0029] a) receiving a biometric information sample from the user for usein identifying the user;

[0030] b) determining electronic data representing said biometricinformation sample of the user;

[0031] c) analyzing The electronic data to compare the electronic dataagainst stored data, the stored data relating to identifications ofusers;

[0032] d) when a substantial match is found, performing the steps of:identifying the individual; storing the electronic data in associationwith the user identification as further stored data; determining fromthe further stored data comparison data for use in identifying the userand different from the stored data: and,

[0033] e) when a difference between the electronic data and the storeddata is above a predetermined other threshold, comparing the electronicdata against other stored data, the other stored data relating lobiometric information samples provided for use in identifying the useraccording to steps (a) to (e).

[0034] In accordance with the invention there is also provided a methodof identifying a user comprising the steps of:

[0035] receiving a biometric information sample from the user for use inidentifying the user;

[0036] determining electronic data representing said biometricinformation sample of the user;

[0037] analyzing the electronic data to extract features for comparisonwith stored electronic data;

[0038] comparing the extracted features against stored electronic data,the stored electronic data relating to identifications of users; when asubstantial match is found, performing the steps of:

[0039] identifying the individual;

[0040] determining a number of times that the user has been identifiedand when the number is one of at least a predetermined number;

[0041] storing the electronic data in association with the useridentification as further stored data;

[0042] determining from the further stored data comparison data for usein identifying the user and different from the stored data; and,

[0043] when a difference between the electronic data and the stored datais above a predetermined other threshold, comparing the electronic dataagainst other stored data, the other stored data relating to biometricinformation samples provided for use in identifying the user.

[0044] In accordance with the invention there is also provided a methodof identifying a user comprising the steps of:

[0045] forming at least a first template including biometric featuresfor use in identifying an individual;

[0046] forming a history file including a plurality of biometricinformation samples each provided by the same individual at differenttimes for use in identifying the individual;

[0047] forming at least an adaptive enrollment template from thebiometric information samples in the history file,

[0048] wherein an individual is identified by registering, providedbiometric information sample against the at least a first template and,when registration is not successful registering the provided biometricinformation sample against the at least an adaptive enrollment template.

BRIEF DESCRIPTION OF THE DRAWING

[0049]FIG. 1 shows a simplified flow diagram of a process for biometricenrollment, according to the prior art;

[0050]FIG. 2 shows a simplified flow diagram of all adaptive biometricenrollment method, according to the present invention;

[0051]FIG. 3 shows a simplified flow diagram of a method for recognizinga user in dependence upon a biometric input, according to the presentinvention: and,

[0052]FIG. 4 shows a simplified flow diagram of a method for comparingbiometric information against three adaptive enrollments updated in acircular fashion, according to the present invention.

DETAILED DESCRIPTION OF THE INVENTION

[0053] The invention is described with respect to finger printregistration. The method of this invention is applicable to otherbiometric verification process as is evident to those of skill in theart.

[0054] There are two common architectures for use with biometricidentification systems, one-to-one and one-to-many. In one-to-onebiometric identification, a user is identified separate from thebiometric identification process and the identification is verified byverifying biometric data provided with a template associated with theidentified individual. Such a system, because only one instance ofsensed biometric information is compared against one template, isrelatively secure. That said, the architecture is not well suited tosome applications. In a one-to-many architecture, a single fingerprintis compared against numerous templates to isolate a template that mostclosely matches biometric information provided. The security of thesystem is often lower than that of a system employing a one-to-onearchitecture but a one-to-many architecture allows for useridentification with no input data other than the biometric informationsample.

[0055] One of the problems with a finger print biometric is that asegment of the population can have temporary skin conditions which causepoor image quality on the scanning device or changes in fingerprintqualities which in turn causes them to experience high false rejectionrates. One method of overcoming this problem is to allow the use of anyfingertip. Unfortunately, such a method results in a large database oftemplates and, thereby results in greatly reduced overall security forone-to-many fingerprint identification systems.

[0056] Referring to FIG. 1, a flow diagram of a prior art embodiment forbiometric identification is shown. Biometric templates are stored via astatic designated biometric enrollment process. This process isconsciously executed each time a user desires to update an individualbiometric template, and involves presenting the specified biometricinformation sample to a sensor when in execution of a custom enrollmentdialog. Since the user consciously executes this enrollment process,additional expertise is implied. Alternatively, the process is executedperiodically by security personnel implying a need for additionalresources.

[0057] The motivation for executing this process at regular intervals isthat any particular biometric information sample, being linked to aconstantly changing organic signature, typically evolves with thepassage of time. In some instances, this leads to a significant increasein the false rejection rate over the span of a few months. The falserejection rate is the ratio of rejections in the form of failedcomparisons to acceptances in the form of accurate comparisons for avalid biometric information sample. This results in reduced usability,significant additional administrative overhead, and frustration.

[0058] In order to improve the biometric enrollment process the presentembodiment provides a method that enables ongoing adaptive enrollment ofbiometric information samples during normal daily operation of abiometric identification system. The adaptive enrollment process istransparent to the user and requires minimal administrative overhead.The false rejection rate is maintained at an approximately constantlevel, improving the usability of the system over prior art systems.Notification is provided to administrators as individual biometricinformation samples begin to vary in a divergent fashion from theoriginally registered biometric information samples, allowingadministrators to schedule static reenrollment of biometric informationsamples in a controlled manner as necessary.

[0059] Referring to FIG. 2, a simplified flow diagram of an embodimentof an adaptive biometric enrollment method is shown. A user providesbiometric information to a contact imaging device. The biometricinformation is imaged and a digital representation of the biometricinformation is formed. The digital representation is then analysed todetermine features forming a part thereof. These features are thencompared to templates of features relating to known individuals. When afeature match occurs within predetermined limits, the user is identifiedas the related known individual.

[0060] Of course, it is well known that biometric information variesover time. For example, if a hand is soaked in water for a length oftime, the fingerprint changes. Similarly, when the weather is extremelydry a fingerprint changes a bit. Further, different imaging devices relyon different phenomena. Some imagers require some moisture toeffectively image but cannot tolerate too much moisture. Of course, asseasons change, moisture levels in the air vary and so do moisturelevels within peoples' skin.

[0061] When features within a digital representation match those of atemplate, a distance between the digital representation and the templateis calculated. This distance is used to determine whether the digitalrepresentation should be stored as a subsidiary template. Adaptiveenrollments in the form of subsidiary templates are useful to allow forcompensation for variations in biometric information that occur overtime. For example, as the weather gets colder, the fingertips grow driercausing effective changes in the imaged fingerprint. By storing newadaptive enrollments in the form of subsidiary templates when a user isidentified, the system is provided with templates that more closelymatch a current biometric information sample of the user.

[0062] In FIG. 2, the digital representation is outside a predetermineddistance from the template and as such is stored as an adaptiveenrollment. The digital representation is also compared to determine adistance from the originally enrolled template. When the distance isbeyond a predetermined maximum distance, security is notified tore-enroll the user to establish a new base template. This preventstemplates from drifting too far from the originally enrolled template.Of course, as shown in FIG. 2, the adaptive enrolment is a firstadaptive enrolment for the tiger and as such the distance from theoriginally enrolled template is within the predetermined limits.

[0063] Referring to FIG. 3, a flow diagram of a method of recognizing auser is shown. A user provides biometric information to a contactimaging device. The biometric information is imaged and a digitalrepresentation of the biometric information is formed. The digitalrepresentation is then analyzed to determine features forming a partthereof. These features are then compared to templates of featuresrelating to known individuals. When a feature match occurs withinpredetermined limits, the user is identified as the related knownindividual.

[0064] In the flow diagram of FIG. 3, none of the original templatesmatch the features within predetermined limits. The closest template isthen selected and the features are compared against adaptive enrolmentsrelating to that template. Alternatively, several closest templates areselected. When a match is determined within the predetermined limits orwithin other predetermined limits depending on design parameters, theuser is identified. Thus there is no performance impact during anauthentication that compares successfully against a statically enrolledbiometric and the performance impact for failed registrations is verysmall. The recognition of individuals who would heretofore have beenfalsely rejected is advantageously achieved.

[0065] In a preferred embodiment, an authentication server databasememory diagram for which is shown in FIG. 4, three classes of biometrictemplate enrollments are stored in the authentication server database. Amaster enrollment, generated from a statically enrolled biometricinformation source, is stored for each biometric. The maser enrollmentsare updated during a static enrollment process. A number of historicalenrollments are stored for each biometric template based on data withina configuration file. The historical enrollments are updated in acircular fashion by replacing an oldest historical enrollment with a newhistorical enrollment. A new historical enrollment is generatedperiodically based on a configuration file defined period. Threeadaptive enrollments, composed of the three historical enrollments withthe highest composite comparison metric, are stored for each biometric.The adaptive enrollments are updated each time a new historicalenrollment is stored. Additional processing required for processingadaptive enrollments typically executes in a background thread on theauthentication server.

[0066] For example, an adaptive enrollment occurs the first time a userattempts to authenticate biometrically after a span if time greater thanthe adaptive enrollment period has passed since the last adaptiveenrollment. The following procedures is followed:

[0067] The provided biometric data in the form of a digitalrepresentation is compared against each template in the set of masterenrolment templates. If a match is found then authentication occurs andthe digital representation is queued for processing by a backgroundthread. If a match is not found then the closest matching template fromtie master enrolment templates is used to determine which biometric waspresented and a master enrollment match failure is logged. The digitalrepresentation is compared against each template in the set of adaptiveenrollments for that biometric. In the example of FIG. 4 that is threetemplates. If a match is found then authentication occurs and theauthentication biometric is queued for processing by the backgroundthread. If a match is not found then authentication fails and thedigital representation is discarded.

[0068] The background thread executes periodically and examines thequeue for any digital representations that are newly added. If the queueis empty then the background thread terminates until it is executedagain after a time period. If the queue is not empty then each digitalrepresentation is processed in turn to form the three adaptiveenrollments.

[0069] When there exist a predetermined number of historical enrollmentsassociated with a template the oldest digital representation isdetermined and is compared against all he other historical enrollmentsin the set in order to calculate a set of comparison metrics. Thecomposite comparison metric for each historical enrollment associatedwith the template is updated by subtracting the relevant comparisonmetric. The oldest digital representation is deleted.

[0070] When there exist fewer than the predetermined number ofhistorical enrollments associated with a template then the digitalrepresentation is compared against all the historical enrollmentsassociated with the master enrollment template in order to calculate aset of comparison metrics. The composite comparison metric for eachhistorical enrollment associated with the template is updated by addingthe relevant comparison metric. The digital representation is stored asa new historical enrollment and its composite comparison metric iscalculated from the average of the individual comparison metrics for allthe other historical enrollments associated with the master template andthe master enrollment template.

[0071] The three historical enrollments with the largest compositecomparison metrics associated with a template are designated theadaptive enrollments associated with the template. These enrollments arccopied and restored in order to optimize their retrieval and comparisonduring the authentication process. Alternatively, they are not copiedand their retrieval and comparison requires additional time.

[0072] In order to describe the adaptive enrollment process, it ishelpful to provide a concise mathematical characterization of apreferred composite comparison metric, as well as the subtraction andaddition operations.

[0073] The composite comparison metric for historical enrollment i isgiven by${m_{i} = \frac{\underset{j \neq 1}{\sum\limits_{j = 1}^{n}}\quad {c\left( {H_{i},H_{j}} \right)}}{n - 1}},{i \in \left\{ {1,\ldots \quad,n} \right\}}$

[0074] where n is the number of historical enrollments in the set andc(H_(i),H_(j)) is the comparison metric between biometric template i andbiometric template j.

[0075] The updated composite comparison metric for historical enrollmenti after the subtraction operation on historical enrollment j is given by${m_{i} = {m_{i} - \frac{c\left( {H_{i},H_{j}} \right)}{n - 1}}},{i \in \left\{ {1,\ldots \quad,n} \right\}},{i \neq j}$

[0076] where n is the number of historical enrollments in the setincluding historical enrollment j.

[0077] The updated composite comparison metric for historical enrollmenti after the addition operation on historical enrollment j is given by${m_{i} = {m_{i} + \frac{c\left( {H_{i},H_{j}} \right)}{n - 1}}},{i \in \left\{ {1,\ldots \quad,n} \right\}},{i \neq j}$

[0078] where n is the number of historical enrollments in the setincluding historical enrollment j.

[0079] Of course, it is also possible to provide a new historicenrolment with each successful identification. Unfortunately, it isgenerally found that such a system results in a history that is eithertoo cumbersome to process or unlikely to be of sufficient duration totrack many slowly varying changes in biometric information. Preferably,historic enrollments are captured one or two times during a week period.

[0080] When the adaptive enrolments are more than a predetermineddistance from the master enrolment templates, security is notified tore-enroll the individual. This prevents slow drifting biometricinformation from overlapping with another user, thereby resulting infalse acceptances.

[0081] Preferably, more than one master enrollment is used. Using, forexample, three master enrollment templates allows for more effectiveuser identification. Alternatively, using more than one masterenrollment template allows for selection of diverse templates that areaccurate for a particular user as master templates in order to provideenhanced space within which to successfully identify a user.

[0082] Though the above method is described with reference to backgroundprocessing and to minimizing performance impact caused by the method,other implementations of the invention are equally possible.

[0083] Numerous other embodiments of the invention may be envisagedwithout departing from the spirit or scope of the invention.

What is claimed is:
 1. A method of identifying a user comprising thesteps of: a) receiving a biometric information sample from the user foruse in identifying the user; b) determining electronic data representingsaid biometric information sample of the user; c) analyzing theelectronic data to compare the electronic data against first stored datarelating to identifications of users; d) when a substantial match isfound, identifying the individual; and, e) when a difference between theelectronic data and the first stored data is above a predetermined otherthreshold, comparing the electronic data against adaptive enrolments,the adaptive enrolments relating to biometric information samplesprovided by the user for use in identifying the user by the steps (a)through (c).
 2. A method according to claim 1 wherein everypredetermined number of occurrences of a substantial match the followingsteps are performed: the electronic data is stored within a historyfile: the electronic data is analyzed to determine its suitability foruse in determining the adaptive enrollment; and, when the electronicdata is determined to be suitable, determining the adaptive enrollmenttherefrom.
 3. A method according to claim 2 wherein the history filecomprises up to N samples of electronic data and wherein an oldestsample of electronic data is deleted when a new sample of electronicdata is added to a history file containing N samples.
 4. A methodaccording to claim 3 wherein a score is stored in association with eachsample electronic data within the history file indicating a suitabilityof the sample for use in determining the adaptive enrolment.
 5. A methodaccording to claim 4 wherein the score is determined by comparing thesample electronic data against each other sample electronic data withinthe history file and associated with a same user identification.
 6. Amethod according to claim 5 wherein the score is determined bydetermining a composite comparison metric, M; for each sample electronicdata, i,${m_{i} = \frac{\underset{j \neq 1}{\sum\limits_{j = 1}^{n}}\quad {c\left( {H_{i},H_{j}} \right)}}{n - 1}},{i \in \left\{ {1,\ldots \quad,n} \right\}}$

where n is the number of historical enrollments in the set andc(H_(i),H_(j)) is the comparison metric between biometric template i andbiometric template j.
 7. A method according to claim 5 wherein upondeleting a sample electronic data from the history file, its effect onthe scores in the history file is approximately reversed.
 8. A methodaccording to claim 7 wherein the effect is approximately reversed byrecalculating m, after the deletion of sample electronic element jaccording to${m_{i} = {m_{i} - \frac{c\left( {H_{i},H_{j}} \right)}{n - 1}}},{i \in \left\{ {1,\ldots \quad,n} \right\}},{i \neq j}$

where n is the number of historical enrollments in the set includinghistorical enrollment j.
 9. A method according to claim 8 wherein theeffect of an added sample electronic element j is determined bycalculating m; according to${m_{i} = {m_{i} + \frac{c\left( {H_{i},H_{j}} \right)}{n - 1}}},{i \in \left\{ {1,\ldots \quad,n} \right\}},{i \neq j}$

where n is the number of historical enrollments in the set includinghistorical enrollment j.
 10. A method according to claim 2 wherein theeffect of an added sample electronic element j is determined bycalculating m; according to${m_{i} = {m_{i} + \frac{c\left( {H_{i},H_{j}} \right)}{n - 1}}},{i \in \left\{ {1,\ldots \quad,n} \right\}},{i \neq j}$

where n is the number of historical enrollments in the set includinghistorical enrollment i.
 11. A method according to claim 2 whereinelectronic data samples suitable for use in determining the adaptiveenrolment are compared against the stored data, and when a differencegreater than a predetermined difference exists, a notification of suchis generated.
 12. A method according to claim 2 wherein electronic datasamples suitable for use in determining the other stored data arecompared against the stored data, and when a difference greater than apredetermined difference exists, the user is prompted to re-enroll inorder to provide new biometric information for use in determining newstored data.
 13. A method of identifying a user comprising the steps of:a) receiving a biometric information sample from the user for use inidentifying the user; b) determining electronic data representing saidbiometric information sample of the user; c) analyzing the electronicdata to compare the electronic data against stored data, the stored datarelating to identifications of users: d) when a substantial match isfound, performing the steps of: identifying the individual; storing theelectronic data in association with the user identification as furtherstored data: determining from the further stored data comparison datafor use in identifying the user and different from the stored data; and,e) wherein a difference between the electronic data and the stored datais above a predetermined other threshold, comparing the electronic dataagainst other stored data, the other stored data relating to biometricinformation samples provided for use in identifying the user accordingto steps (a) to (e).
 14. A method according to claim 13 wherein thefurther stored data relates to different features of the individual, thefeatures for use in identifying the individual.
 15. A method accordingto claim 13 wherein the step of storing the electronic data inassociation with the user identification as further stored datacomprises the step of: storing an electronic representation of thebiometric information sample associated with the identification.
 16. Amethod according to claim 15 wherein the step of determining from thefurther stored data comparison data for use in identifying the user anddifferent from the stored data is performed by the steps of: comparingeach stored electronic representation associated with the identificationagainst a template derived from each other electronic representationassociated with the identification to select at least an electronicrepresentation that best represents the stored electronicrepresentations associated with the identification; and, determining atleast a template from the at least a selected electronic representation.17. A method according to claim 15 wherein the step of determining fromthe further stored data comparison data for use in identifying the userand different from the stored data is performed by the steps of: storingwith each electronic representation associated with the identification ascore indicative of a quality of the stored electronic representationsassociated with the identification for use in generating a template;comparing the oldest stored electronic representation associated withthe identification against a template derived from each other electronicrepresentation associated with the identification to determine thecorrection for the score associated with said template; correcting eachscore in accordance with the determined corrections; comparing thenewest stored electronic representation associated with theidentification against a template derived from each other electronicrepresentation associated with the identification to determine a secondcorrection for each score; correcting each score in accordance with thedetermined second correction; and, selecting an electronicrepresentation associated with a best score for use in determining atemplate for use as the comparison data.
 18. A method of identifying auser comprising the steps of: receiving a biometric information samplefrom the user for use in identifying the user; determining electronicdata representing said biometric information sample of the user;analyzing the electronic data to extract features for comparison withstored electronic data; comparing the extracted features against storedelectronic data, the stored electronic data relating to identificationsof users; when a substantial match is found, performing the steps of:identifying the individual; determining a number of times that the userhas been identified and when the number is one of at least apredetermined number; storing the electronic data in association withthe user identification as further stored data; determining from thefurther stored data comparison data for use in identifying the user anddifferent from the data; and, when a difference between the electronicdata and the stored data is above a predetermined other threshold,comparing the electronic data against other stored data, the otherstored data relating to biometric information samples provided for usein identifying the user.
 19. A method of identifying a user comprisingthe steps of: forming at least a first template including biometricfeatures for use in identifying individual: forming a history fileincluding a plurality of biometric information samples each provided bythe same individual at different limes for use in identifying theindividual; forming at least an adaptive enrollment template from thebiometric information samples in the history file, wherein an individualis identified by registering provided biometric information sampleagainst the at least a first template and, when registration is notsuccessful registering the provided biometric information sample againstthe at least adaptive enrollment template.
 20. A method according toclaim 19 wherein the at least an adaptive enrollment template is atemplate formed from one or more biometric information samples withinthe history file, the one or more biometric information samples selectedthrough a comparison with other biometric information samples within thehistory file.
 21. A method according to claim 20 wherein each biometricinformation sample stored within the history file is compared againsteach other biometric information sample within the history file todetermine those that are most suitable for use in determining adaptiveenrollment templates.
 22. A method according to claim 21 wherein thehistory file comprises up to N biometric information samples and whereinan oldest biometric information sample is deleted when a new biometricinformation sample is added to a history file containing N samples. 23.A method according to claim 22 wherein a score is stored in associationwith each biometric information sample within the history fileindicating a suitability of that sample for use in determining anadaptive enrollment template.
 24. A method according to claim 23 whereinthe score is determined by comparing the sample electronic data againsteach other sample electronic data within the history file and associatedwith a same user identification.
 25. A method according to claim 24wherein the score is determined by determining a composite comparisonmetric, M; for each sample electronic data, i,${m_{i} = \frac{\underset{j \neq 1}{\sum\limits_{j = 1}^{n}}\quad {c\left( {H_{i},H_{j}} \right)}}{n - 1}},{i \in \left\{ {1,\ldots \quad,n} \right\}}$

where n is the number of historical enrollments in the set andc(H_(i),H_(j)) is the comparison metric between biometric template i andbiometric template j.
 26. A method according to claim 24 wherein upondeleting a sample electronic data from the history file, its effect onthe scores in the history file is approximately reversed.
 27. A methodaccording to claim 26 wherein the effect is approximately reversed byrecalculating m_(i) after the deletion of sample electronic element jaccording to${m_{i} = {m_{i} - \frac{c\left( {H_{i},H_{j}} \right)}{n - 1}}},{i \in \left\{ {1,\ldots \quad,n} \right\}},{i \neq j}$

where n is the number of historical enrollments in the set includinghistorical enrollment.
 28. A method according to claim 27 wherein theeffect of an added sample electronic element j is determined bycalculating m_(i) according to${m_{i} = {m_{i} + \frac{c\left( {H_{i},H_{j}} \right)}{n - 1}}},{i \in \left\{ {1,\ldots \quad,n} \right\}},{i \neq j}$

wherein n is the number of historical enrollments in the set includinghistorical enrollment.
 29. A method according to claim 28 wherein theeffect of an added sample electronic element j is determined bycalculating mi_(i) according to${m_{i} = {m_{i} + \frac{c\left( {H_{i},H_{j}} \right)}{n - 1}}},{i \in \left\{ {1,\ldots \quad,n} \right\}},{i \neq j}$

where n is the number of historical enrollments in the set includinghistorical enrollment.
 30. A method according to claim 19 whereinadaptive enrollment templates are compared against the at least a firsttemplate and, when a difference greater than a predetermined differenceexists, a notification of such is generated.
 31. A method according toclaim 19 wherein adaptive enrollment templates are compared against theat least a first template and, when a difference greater than apredetermined difference exists, the user is prompted to re-enroll inorder to provide new biometric information for use in determining a newat least at first template.